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blueprints
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v0.22.0
| Author | SHA1 | Date | |
|---|---|---|---|
| a8d2519058 | |||
| 4efe1ddb5c | |||
| f9c84c94b4 | |||
| 78b5dec6b6 | |||
| 72e3f6081c | |||
| 7ec7b6ffe9 | |||
| 6887165a9d | |||
| cc4d711eb1 | |||
| 626b082838 | |||
| d0328b442d |
File diff suppressed because it is too large
Load Diff
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"label": "c",
|
||||
"localized_name": "values.c",
|
||||
"name": "values.c",
|
||||
"shape": 7,
|
||||
"type": "FLOAT,INT,BOOLEAN",
|
||||
"link": null
|
||||
},
|
||||
{
|
||||
"localized_name": "expression",
|
||||
"name": "expression",
|
||||
"type": "STRING",
|
||||
"widget": {
|
||||
"name": "expression"
|
||||
},
|
||||
"link": null
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "FLOAT",
|
||||
"name": "FLOAT",
|
||||
"type": "FLOAT",
|
||||
"links": null
|
||||
},
|
||||
{
|
||||
"localized_name": "INT",
|
||||
"name": "INT",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
403
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "BOOL",
|
||||
"name": "BOOL",
|
||||
"type": "BOOLEAN",
|
||||
"links": null
|
||||
}
|
||||
],
|
||||
"title": "Math Expression （Width）",
|
||||
"properties": {
|
||||
"Node name for S&R": "ComfyMathExpression",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.18.1",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
"hasSecondTab": false,
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65,
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.7",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
"max(1, (int(a) + int(b) - 1) // int(b))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 228,
|
||||
"type": "PrimitiveInt",
|
||||
"pos": [
|
||||
-1380,
|
||||
90
|
||||
],
|
||||
"size": [
|
||||
230,
|
||||
110
|
||||
],
|
||||
"flags": {},
|
||||
"order": 1,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "value",
|
||||
"name": "value",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "value"
|
||||
},
|
||||
"link": 427
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "INT",
|
||||
"name": "INT",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
388
|
||||
]
|
||||
}
|
||||
],
|
||||
"title": "Int (grid columns)",
|
||||
"properties": {
|
||||
"Node name for S&R": "Int (grid columns)",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.18.1",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
"hasSecondTab": false,
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65,
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.7",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
2,
|
||||
"fixed"
|
||||
]
|
||||
},
|
||||
{
|
||||
"id": 230,
|
||||
"type": "GetImageSize",
|
||||
"pos": [
|
||||
-1380,
|
||||
290
|
||||
],
|
||||
"size": [
|
||||
230,
|
||||
100
|
||||
],
|
||||
"flags": {},
|
||||
"order": 3,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "image",
|
||||
"name": "image",
|
||||
"type": "IMAGE",
|
||||
"link": 389
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "width",
|
||||
"name": "width",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
387
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "height",
|
||||
"name": "height",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
390
|
||||
]
|
||||
},
|
||||
{
|
||||
"localized_name": "batch_size",
|
||||
"name": "batch_size",
|
||||
"type": "INT",
|
||||
"links": null
|
||||
}
|
||||
],
|
||||
"properties": {
|
||||
"Node name for S&R": "GetImageSize",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.18.1",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
"hasSecondTab": false,
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65,
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.7",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 252,
|
||||
"type": "PrimitiveInt",
|
||||
"pos": [
|
||||
-1380,
|
||||
470
|
||||
],
|
||||
"size": [
|
||||
230,
|
||||
110
|
||||
],
|
||||
"flags": {},
|
||||
"order": 5,
|
||||
"mode": 0,
|
||||
"inputs": [
|
||||
{
|
||||
"localized_name": "value",
|
||||
"name": "value",
|
||||
"type": "INT",
|
||||
"widget": {
|
||||
"name": "value"
|
||||
},
|
||||
"link": 428
|
||||
}
|
||||
],
|
||||
"outputs": [
|
||||
{
|
||||
"localized_name": "INT",
|
||||
"name": "INT",
|
||||
"type": "INT",
|
||||
"links": [
|
||||
429
|
||||
]
|
||||
}
|
||||
],
|
||||
"title": "Int (grid rows)",
|
||||
"properties": {
|
||||
"Node name for S&R": "Int (grid rows)",
|
||||
"cnr_id": "comfy-core",
|
||||
"ver": "0.18.1",
|
||||
"enableTabs": false,
|
||||
"tabWidth": 65,
|
||||
"tabXOffset": 10,
|
||||
"hasSecondTab": false,
|
||||
"secondTabText": "Send Back",
|
||||
"secondTabOffset": 80,
|
||||
"secondTabWidth": 65,
|
||||
"ue_properties": {
|
||||
"widget_ue_connectable": {},
|
||||
"version": "7.7",
|
||||
"input_ue_unconnectable": {}
|
||||
}
|
||||
},
|
||||
"widgets_values": [
|
||||
3,
|
||||
"fixed"
|
||||
]
|
||||
}
|
||||
],
|
||||
"groups": [],
|
||||
"links": [
|
||||
{
|
||||
"id": 403,
|
||||
"origin_id": 229,
|
||||
"origin_slot": 1,
|
||||
"target_id": 225,
|
||||
"target_slot": 1,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 404,
|
||||
"origin_id": 231,
|
||||
"origin_slot": 1,
|
||||
"target_id": 225,
|
||||
"target_slot": 2,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 390,
|
||||
"origin_id": 230,
|
||||
"origin_slot": 1,
|
||||
"target_id": 231,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 387,
|
||||
"origin_id": 230,
|
||||
"origin_slot": 0,
|
||||
"target_id": 229,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 388,
|
||||
"origin_id": 228,
|
||||
"origin_slot": 0,
|
||||
"target_id": 229,
|
||||
"target_slot": 1,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 386,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 225,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 389,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 0,
|
||||
"target_id": 230,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 394,
|
||||
"origin_id": 225,
|
||||
"origin_slot": 0,
|
||||
"target_id": -20,
|
||||
"target_slot": 0,
|
||||
"type": "IMAGE"
|
||||
},
|
||||
{
|
||||
"id": 427,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 1,
|
||||
"target_id": 228,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 428,
|
||||
"origin_id": -10,
|
||||
"origin_slot": 2,
|
||||
"target_id": 252,
|
||||
"target_slot": 0,
|
||||
"type": "INT"
|
||||
},
|
||||
{
|
||||
"id": 429,
|
||||
"origin_id": 252,
|
||||
"origin_slot": 0,
|
||||
"target_id": 231,
|
||||
"target_slot": 1,
|
||||
"type": "INT"
|
||||
}
|
||||
],
|
||||
"extra": {},
|
||||
"category": "Image Tools/Crop"
|
||||
}
|
||||
]
|
||||
},
|
||||
"extra": {}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -152,6 +152,11 @@ class StableAudio1(LatentFormat):
|
||||
latent_dimensions = 1
|
||||
temporal_downscale_ratio = 2048
|
||||
|
||||
class StableAudio3(LatentFormat):
|
||||
latent_channels = 256
|
||||
latent_dimensions = 1
|
||||
temporal_downscale_ratio = 4096
|
||||
|
||||
class Flux(SD3):
|
||||
latent_channels = 16
|
||||
def __init__(self):
|
||||
|
||||
@ -10,6 +10,17 @@ from torch import nn
|
||||
from torch.nn import functional as F
|
||||
import math
|
||||
import comfy.ops
|
||||
from .embedders import ExpoFourierFeatures
|
||||
|
||||
|
||||
def _left_pad_to_match(emb, target_len):
|
||||
emb_len = emb.shape[-2]
|
||||
if emb_len < target_len:
|
||||
return F.pad(emb, (0, 0, target_len - emb_len, 0), value=0.)
|
||||
elif emb_len > target_len:
|
||||
return emb[:, -target_len:, :]
|
||||
return emb
|
||||
|
||||
|
||||
class FourierFeatures(nn.Module):
|
||||
def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
|
||||
@ -22,6 +33,7 @@ class FourierFeatures(nn.Module):
|
||||
f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
|
||||
return torch.cat([f.cos(), f.sin()], dim=-1)
|
||||
|
||||
|
||||
# norms
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
|
||||
@ -43,6 +55,16 @@ class LayerNorm(nn.Module):
|
||||
beta = comfy.ops.cast_to_input(beta, x)
|
||||
return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)
|
||||
|
||||
|
||||
class RMSNorm(nn.Module):
|
||||
def __init__(self, dim, dtype=None, device=None):
|
||||
super().__init__()
|
||||
self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
|
||||
|
||||
def forward(self, x):
|
||||
return F.rms_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x))
|
||||
|
||||
|
||||
class GLU(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
@ -236,13 +258,6 @@ class FeedForward(nn.Module):
|
||||
|
||||
linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
|
||||
|
||||
# # init last linear layer to 0
|
||||
# if zero_init_output:
|
||||
# nn.init.zeros_(linear_out.weight)
|
||||
# if not no_bias:
|
||||
# nn.init.zeros_(linear_out.bias)
|
||||
|
||||
|
||||
self.ff = nn.Sequential(
|
||||
linear_in,
|
||||
rearrange('b d n -> b n d') if use_conv else nn.Identity(),
|
||||
@ -261,8 +276,10 @@ class Attention(nn.Module):
|
||||
dim_context = None,
|
||||
causal = False,
|
||||
zero_init_output=True,
|
||||
qk_norm = False,
|
||||
qk_norm = "none",
|
||||
differential = False,
|
||||
natten_kernel_size = None,
|
||||
feat_scale = False,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=None,
|
||||
@ -271,6 +288,7 @@ class Attention(nn.Module):
|
||||
self.dim = dim
|
||||
self.dim_heads = dim_heads
|
||||
self.causal = causal
|
||||
self.differential = differential
|
||||
|
||||
dim_kv = dim_context if dim_context is not None else dim
|
||||
|
||||
@ -278,18 +296,37 @@ class Attention(nn.Module):
|
||||
self.kv_heads = dim_kv // dim_heads
|
||||
|
||||
if dim_context is not None:
|
||||
self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
|
||||
if differential:
|
||||
self.to_q = operations.Linear(dim, dim * 2, bias=False, dtype=dtype, device=device)
|
||||
self.to_kv = operations.Linear(dim_kv, dim_kv * 3, bias=False, dtype=dtype, device=device)
|
||||
else:
|
||||
self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
|
||||
else:
|
||||
self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
|
||||
if differential:
|
||||
self.to_qkv = operations.Linear(dim, dim * 5, bias=False, dtype=dtype, device=device)
|
||||
else:
|
||||
self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
# if zero_init_output:
|
||||
# nn.init.zeros_(self.to_out.weight)
|
||||
|
||||
# Accept bool for backward compat
|
||||
if isinstance(qk_norm, bool):
|
||||
qk_norm = "l2" if qk_norm else "none"
|
||||
self.qk_norm = qk_norm
|
||||
|
||||
if self.qk_norm == "ln":
|
||||
self.q_norm = operations.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
|
||||
self.k_norm = operations.LayerNorm(dim_heads, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
|
||||
elif self.qk_norm == "rms":
|
||||
self.q_norm = RMSNorm(dim_heads, dtype=dtype, device=device)
|
||||
self.k_norm = RMSNorm(dim_heads, dtype=dtype, device=device)
|
||||
|
||||
self.feat_scale = feat_scale
|
||||
|
||||
if self.feat_scale:
|
||||
self.lambda_dc = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
|
||||
self.lambda_hf = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
|
||||
|
||||
def forward(
|
||||
self,
|
||||
@ -306,22 +343,51 @@ class Attention(nn.Module):
|
||||
kv_input = context if has_context else x
|
||||
|
||||
if hasattr(self, 'to_q'):
|
||||
# Use separate linear projections for q and k/v
|
||||
q = self.to_q(x)
|
||||
q = rearrange(q, 'b n (h d) -> b h n d', h = h)
|
||||
if self.differential:
|
||||
# cross-attention differential: to_q → (q, q_diff), to_kv → (k, k_diff, v)
|
||||
q, q_diff = self.to_q(x).chunk(2, dim=-1)
|
||||
q = rearrange(q, 'b n (h d) -> b h n d', h=h)
|
||||
q_diff = rearrange(q_diff, 'b n (h d) -> b h n d', h=h)
|
||||
q = torch.stack([q, q_diff], dim=1) # (B, 2, H, N, D)
|
||||
k, k_diff, v = self.to_kv(kv_input).chunk(3, dim=-1)
|
||||
k = rearrange(k, 'b n (h d) -> b h n d', h=kv_h)
|
||||
k_diff = rearrange(k_diff, 'b n (h d) -> b h n d', h=kv_h)
|
||||
v = rearrange(v, 'b n (h d) -> b h n d', h=kv_h)
|
||||
k = torch.stack([k, k_diff], dim=1) # (B, 2, H, M, D)
|
||||
else:
|
||||
# Use separate linear projections for q and k/v
|
||||
q = self.to_q(x)
|
||||
q = rearrange(q, 'b n (h d) -> b h n d', h = h)
|
||||
|
||||
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
||||
k, v = self.to_kv(kv_input).chunk(2, dim=-1)
|
||||
|
||||
k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
|
||||
k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
|
||||
else:
|
||||
# Use fused linear projection
|
||||
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
|
||||
if self.differential:
|
||||
# self-attention differential: to_qkv → (q, k, v, q_diff, k_diff)
|
||||
q, k, v, q_diff, k_diff = self.to_qkv(x).chunk(5, dim=-1)
|
||||
q, k, v, q_diff, k_diff = map(
|
||||
lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h),
|
||||
(q, k, v, q_diff, k_diff)
|
||||
)
|
||||
q = torch.stack([q, q_diff], dim=1) # (B, 2, H, N, D)
|
||||
k = torch.stack([k, k_diff], dim=1)
|
||||
else:
|
||||
# Use fused linear projection
|
||||
q, k, v = self.to_qkv(x).chunk(3, dim=-1)
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
|
||||
|
||||
# Normalize q and k for cosine sim attention
|
||||
if self.qk_norm:
|
||||
if self.qk_norm == "l2":
|
||||
q = F.normalize(q, dim=-1)
|
||||
k = F.normalize(k, dim=-1)
|
||||
elif self.qk_norm == "rms":
|
||||
q_type, k_type = q.dtype, k.dtype
|
||||
q = self.q_norm(q).to(q_type)
|
||||
k = self.k_norm(k).to(k_type)
|
||||
elif self.qk_norm != 'none':
|
||||
q = self.q_norm(q)
|
||||
k = self.k_norm(k)
|
||||
|
||||
if rotary_pos_emb is not None and not has_context:
|
||||
freqs, _ = rotary_pos_emb
|
||||
@ -364,9 +430,24 @@ class Attention(nn.Module):
|
||||
heads_per_kv_head = h // kv_h
|
||||
k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
|
||||
|
||||
out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
|
||||
if self.differential:
|
||||
q, q_diff = q.unbind(dim=1)
|
||||
k, k_diff = k.unbind(dim=1)
|
||||
out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
|
||||
out_diff = optimized_attention(q_diff, k_diff, v, h, skip_reshape=True, transformer_options=transformer_options)
|
||||
out = out - out_diff
|
||||
else:
|
||||
out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
|
||||
|
||||
out = self.to_out(out)
|
||||
|
||||
if self.feat_scale:
|
||||
out_dc = out.mean(dim=-2, keepdim=True)
|
||||
out_hf = out - out_dc
|
||||
|
||||
# Selectively modulate DC and high frequency components
|
||||
out = out + comfy.ops.cast_to_input(self.lambda_dc, out) * out_dc + comfy.ops.cast_to_input(self.lambda_hf, out) * out_hf
|
||||
|
||||
if mask is not None:
|
||||
mask = rearrange(mask, 'b n -> b n 1')
|
||||
out = out.masked_fill(~mask, 0.)
|
||||
@ -417,11 +498,14 @@ class TransformerBlock(nn.Module):
|
||||
cross_attend = False,
|
||||
dim_context = None,
|
||||
global_cond_dim = None,
|
||||
global_cond_shared_embed = False,
|
||||
local_add_cond_dim = None,
|
||||
causal = False,
|
||||
zero_init_branch_outputs = True,
|
||||
conformer = False,
|
||||
layer_ix = -1,
|
||||
remove_norms = False,
|
||||
norm_type = "layer_norm",
|
||||
attn_kwargs = {},
|
||||
ff_kwargs = {},
|
||||
norm_kwargs = {},
|
||||
@ -436,8 +520,20 @@ class TransformerBlock(nn.Module):
|
||||
self.cross_attend = cross_attend
|
||||
self.dim_context = dim_context
|
||||
self.causal = causal
|
||||
self.global_cond_shared_embed = global_cond_shared_embed
|
||||
|
||||
self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
|
||||
norm_layer_map = {
|
||||
"layer_norm": LayerNorm,
|
||||
"rms_norm": RMSNorm,
|
||||
}
|
||||
norm_cls = norm_layer_map.get(norm_type, LayerNorm)
|
||||
|
||||
def make_norm():
|
||||
if remove_norms:
|
||||
return nn.Identity()
|
||||
return norm_cls(dim, dtype=dtype, device=device, **norm_kwargs)
|
||||
|
||||
self.pre_norm = make_norm()
|
||||
|
||||
self.self_attn = Attention(
|
||||
dim,
|
||||
@ -451,7 +547,7 @@ class TransformerBlock(nn.Module):
|
||||
)
|
||||
|
||||
if cross_attend:
|
||||
self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
|
||||
self.cross_attend_norm = make_norm()
|
||||
self.cross_attn = Attention(
|
||||
dim,
|
||||
dim_heads = dim_heads,
|
||||
@ -464,37 +560,56 @@ class TransformerBlock(nn.Module):
|
||||
**attn_kwargs
|
||||
)
|
||||
|
||||
self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
|
||||
self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
|
||||
self.ff_norm = make_norm()
|
||||
self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations, **ff_kwargs)
|
||||
|
||||
self.layer_ix = layer_ix
|
||||
|
||||
self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
|
||||
|
||||
self.global_cond_dim = global_cond_dim
|
||||
# Global conditioning
|
||||
self.has_global_cond = (global_cond_dim is not None) or global_cond_shared_embed
|
||||
|
||||
if global_cond_dim is not None:
|
||||
if global_cond_shared_embed:
|
||||
# SA3 style: learnable per-block additive bias; global_cond is pre-projected to (B, dim*6)
|
||||
self.to_scale_shift_gate = nn.Parameter(torch.empty(dim * 6, device=device, dtype=dtype))
|
||||
elif global_cond_dim is not None:
|
||||
# SA1 style: per-block MLP projects global_cond → (B, dim*6)
|
||||
self.to_scale_shift_gate = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
nn.Linear(global_cond_dim, dim * 6, bias=False)
|
||||
operations.Linear(global_cond_dim, dim * 6, bias=False, device=device, dtype=dtype)
|
||||
)
|
||||
|
||||
nn.init.zeros_(self.to_scale_shift_gate[1].weight)
|
||||
#nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
|
||||
# Local additive conditioning (e.g. inpaint mask + masked latent)
|
||||
self.local_add_cond_dim = local_add_cond_dim
|
||||
if local_add_cond_dim is not None:
|
||||
self.to_local_embed = nn.Sequential(
|
||||
operations.Linear(local_add_cond_dim, dim, bias=True, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Linear(dim, dim, bias=True, dtype=dtype, device=device),
|
||||
)
|
||||
else:
|
||||
self.to_local_embed = None
|
||||
|
||||
def forward(
|
||||
self,
|
||||
x,
|
||||
context = None,
|
||||
global_cond=None,
|
||||
local_add_cond=None,
|
||||
mask = None,
|
||||
context_mask = None,
|
||||
rotary_pos_emb = None,
|
||||
transformer_options={}
|
||||
):
|
||||
if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
|
||||
if self.has_global_cond and global_cond is not None:
|
||||
if self.global_cond_shared_embed:
|
||||
# global_cond already has shape (B, dim*6)
|
||||
ssg = (comfy.ops.cast_to_input(self.to_scale_shift_gate, global_cond) + global_cond).unsqueeze(1)
|
||||
else:
|
||||
ssg = self.to_scale_shift_gate(global_cond).unsqueeze(1)
|
||||
|
||||
scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
|
||||
scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = ssg.chunk(6, dim = -1)
|
||||
|
||||
# self-attention with adaLN
|
||||
residual = x
|
||||
@ -510,6 +625,9 @@ class TransformerBlock(nn.Module):
|
||||
if self.conformer is not None:
|
||||
x = x + self.conformer(x)
|
||||
|
||||
if local_add_cond is not None and self.to_local_embed is not None:
|
||||
x = x + _left_pad_to_match(self.to_local_embed(local_add_cond), x.shape[-2])
|
||||
|
||||
# feedforward with adaLN
|
||||
residual = x
|
||||
x = self.ff_norm(x)
|
||||
@ -527,6 +645,9 @@ class TransformerBlock(nn.Module):
|
||||
if self.conformer is not None:
|
||||
x = x + self.conformer(x)
|
||||
|
||||
if local_add_cond is not None and self.to_local_embed is not None:
|
||||
x = x + _left_pad_to_match(self.to_local_embed(local_add_cond), x.shape[-2])
|
||||
|
||||
x = x + self.ff(self.ff_norm(x))
|
||||
|
||||
return x
|
||||
@ -543,6 +664,8 @@ class ContinuousTransformer(nn.Module):
|
||||
cross_attend=False,
|
||||
cond_token_dim=None,
|
||||
global_cond_dim=None,
|
||||
global_cond_shared_embed=False,
|
||||
local_add_cond_dim=None,
|
||||
causal=False,
|
||||
rotary_pos_emb=True,
|
||||
zero_init_branch_outputs=True,
|
||||
@ -550,6 +673,7 @@ class ContinuousTransformer(nn.Module):
|
||||
use_sinusoidal_emb=False,
|
||||
use_abs_pos_emb=False,
|
||||
abs_pos_emb_max_length=10000,
|
||||
num_memory_tokens=0,
|
||||
dtype=None,
|
||||
device=None,
|
||||
operations=None,
|
||||
@ -562,6 +686,8 @@ class ContinuousTransformer(nn.Module):
|
||||
self.depth = depth
|
||||
self.causal = causal
|
||||
self.layers = nn.ModuleList([])
|
||||
self.num_memory_tokens = num_memory_tokens
|
||||
self.global_cond_shared_embed = global_cond_shared_embed
|
||||
|
||||
self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
|
||||
self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
|
||||
@ -577,7 +703,22 @@ class ContinuousTransformer(nn.Module):
|
||||
|
||||
self.use_abs_pos_emb = use_abs_pos_emb
|
||||
if use_abs_pos_emb:
|
||||
self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
|
||||
self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length + num_memory_tokens)
|
||||
|
||||
if num_memory_tokens > 0:
|
||||
self.memory_tokens = nn.Parameter(torch.empty(num_memory_tokens, dim, device=device, dtype=dtype))
|
||||
|
||||
# Shared global-cond embedder (SA3 style): projects (B, global_cond_dim) → (B, dim*6)
|
||||
self.global_cond_embedder = None
|
||||
if global_cond_shared_embed and global_cond_dim is not None:
|
||||
self.global_cond_embedder = nn.Sequential(
|
||||
operations.Linear(global_cond_dim, dim, bias=True, dtype=dtype, device=device),
|
||||
nn.SiLU(),
|
||||
operations.Linear(dim, dim * 6, bias=True, dtype=dtype, device=device),
|
||||
)
|
||||
|
||||
# When using shared embed, TransformerBlocks use per-block Parameter (not per-block MLP)
|
||||
block_global_cond_dim = None if global_cond_shared_embed else global_cond_dim
|
||||
|
||||
for i in range(depth):
|
||||
self.layers.append(
|
||||
@ -586,7 +727,9 @@ class ContinuousTransformer(nn.Module):
|
||||
dim_heads = dim_heads,
|
||||
cross_attend = cross_attend,
|
||||
dim_context = cond_token_dim,
|
||||
global_cond_dim = global_cond_dim,
|
||||
global_cond_dim = block_global_cond_dim,
|
||||
global_cond_shared_embed = global_cond_shared_embed,
|
||||
local_add_cond_dim = local_add_cond_dim,
|
||||
causal = causal,
|
||||
zero_init_branch_outputs = zero_init_branch_outputs,
|
||||
conformer=conformer,
|
||||
@ -605,6 +748,7 @@ class ContinuousTransformer(nn.Module):
|
||||
prepend_embeds = None,
|
||||
prepend_mask = None,
|
||||
global_cond = None,
|
||||
local_add_cond = None,
|
||||
return_info = False,
|
||||
**kwargs
|
||||
):
|
||||
@ -632,7 +776,9 @@ class ContinuousTransformer(nn.Module):
|
||||
|
||||
mask = torch.cat((prepend_mask, mask), dim = -1)
|
||||
|
||||
# Attention layers
|
||||
if self.num_memory_tokens > 0:
|
||||
memory_tokens = comfy.ops.cast_to_input(self.memory_tokens, x).expand(batch, -1, -1)
|
||||
x = torch.cat((memory_tokens, x), dim=1)
|
||||
|
||||
if self.rotary_pos_emb is not None:
|
||||
rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)
|
||||
@ -642,6 +788,10 @@ class ContinuousTransformer(nn.Module):
|
||||
if self.use_sinusoidal_emb or self.use_abs_pos_emb:
|
||||
x = x + self.pos_emb(x)
|
||||
|
||||
# Project global_cond once (SA3 shared-embed path)
|
||||
if global_cond is not None and self.global_cond_embedder is not None:
|
||||
global_cond = self.global_cond_embedder(global_cond)
|
||||
|
||||
blocks_replace = patches_replace.get("dit", {})
|
||||
# Iterate over the transformer layers
|
||||
for i, layer in enumerate(self.layers):
|
||||
@ -654,12 +804,17 @@ class ContinuousTransformer(nn.Module):
|
||||
out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
|
||||
x = out["img"]
|
||||
else:
|
||||
x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
|
||||
# x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
|
||||
x = layer(x, rotary_pos_emb=rotary_pos_emb, global_cond=global_cond,
|
||||
local_add_cond=local_add_cond, context=context,
|
||||
transformer_options=transformer_options)
|
||||
|
||||
if return_info:
|
||||
info["hidden_states"].append(x)
|
||||
|
||||
# Strip memory tokens before projecting out
|
||||
if self.num_memory_tokens > 0:
|
||||
x = x[:, self.num_memory_tokens:, :]
|
||||
|
||||
x = self.project_out(x)
|
||||
|
||||
if return_info:
|
||||
@ -682,6 +837,7 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
num_heads=24,
|
||||
transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
|
||||
global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
|
||||
timestep_features_type: str = "learned",
|
||||
audio_model="",
|
||||
dtype=None,
|
||||
device=None,
|
||||
@ -696,7 +852,10 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
# Timestep embeddings
|
||||
timestep_features_dim = 256
|
||||
|
||||
self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
|
||||
if timestep_features_type == "expo":
|
||||
self.timestep_features = ExpoFourierFeatures(timestep_features_dim, 0.5, 10000.0)
|
||||
else:
|
||||
self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
|
||||
|
||||
self.to_timestep_embed = nn.Sequential(
|
||||
operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
|
||||
@ -781,6 +940,7 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
cross_attn_cond=None,
|
||||
cross_attn_cond_mask=None,
|
||||
input_concat_cond=None,
|
||||
local_add_cond=None,
|
||||
global_embed=None,
|
||||
prepend_cond=None,
|
||||
prepend_cond_mask=None,
|
||||
@ -802,9 +962,13 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
prepend_cond = self.to_prepend_embed(prepend_cond)
|
||||
|
||||
prepend_inputs = prepend_cond
|
||||
prepend_length = prepend_cond.shape[1]
|
||||
if prepend_cond_mask is not None:
|
||||
prepend_mask = prepend_cond_mask
|
||||
|
||||
if local_add_cond is not None and local_add_cond.dim() == 3:
|
||||
local_add_cond = local_add_cond.permute(0, 2, 1)
|
||||
|
||||
if input_concat_cond is not None:
|
||||
|
||||
# Interpolate input_concat_cond to the same length as x
|
||||
@ -850,7 +1014,7 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
if self.transformer_type == "x-transformers":
|
||||
output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
|
||||
elif self.transformer_type == "continuous_transformer":
|
||||
output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
|
||||
output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, local_add_cond=local_add_cond, **extra_args, **kwargs)
|
||||
|
||||
if return_info:
|
||||
output, info = output
|
||||
@ -876,6 +1040,7 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
context=None,
|
||||
context_mask=None,
|
||||
input_concat_cond=None,
|
||||
local_add_cond=None,
|
||||
global_embed=None,
|
||||
negative_global_embed=None,
|
||||
prepend_cond=None,
|
||||
@ -890,6 +1055,7 @@ class AudioDiffusionTransformer(nn.Module):
|
||||
cross_attn_cond=context,
|
||||
cross_attn_cond_mask=context_mask,
|
||||
input_concat_cond=input_concat_cond,
|
||||
local_add_cond=local_add_cond,
|
||||
global_embed=global_embed,
|
||||
prepend_cond=prepend_cond,
|
||||
prepend_cond_mask=prepend_cond_mask,
|
||||
|
||||
@ -31,15 +31,39 @@ def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
|
||||
)
|
||||
|
||||
|
||||
class ExpoFourierFeatures(nn.Module):
|
||||
"""Exponentially-spaced Fourier features (no learnable parameters)."""
|
||||
def __init__(self, dim, min_freq=0.5, max_freq=10000.0):
|
||||
super().__init__()
|
||||
self.dim = dim
|
||||
self.min_freq = min_freq
|
||||
self.max_freq = max_freq
|
||||
|
||||
def forward(self, t):
|
||||
in_dtype = t.dtype
|
||||
t = t.float()
|
||||
if t.dim() == 1:
|
||||
t = t.unsqueeze(-1)
|
||||
half_dim = self.dim // 2
|
||||
ramp = torch.linspace(0, 1, half_dim, device=t.device, dtype=torch.float32)
|
||||
freqs = torch.exp(ramp * (math.log(self.max_freq) - math.log(self.min_freq)) + math.log(self.min_freq))
|
||||
args = t * freqs * 2 * math.pi
|
||||
return torch.cat([args.cos(), args.sin()], dim=-1).to(in_dtype)
|
||||
|
||||
|
||||
class NumberEmbedder(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
features: int,
|
||||
dim: int = 256,
|
||||
fourier_features_type="learned",
|
||||
):
|
||||
super().__init__()
|
||||
self.features = features
|
||||
self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
|
||||
if fourier_features_type == "expo":
|
||||
self.embedding = nn.Sequential(ExpoFourierFeatures(dim=dim), comfy.ops.manual_cast.Linear(in_features=dim, out_features=features))
|
||||
else:
|
||||
self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
|
||||
|
||||
def forward(self, x: Union[List[float], Tensor]) -> Tensor:
|
||||
if not torch.is_tensor(x):
|
||||
@ -77,14 +101,15 @@ class NumberConditioner(Conditioner):
|
||||
def __init__(self,
|
||||
output_dim: int,
|
||||
min_val: float=0,
|
||||
max_val: float=1
|
||||
max_val: float=1,
|
||||
fourier_features_type: str = "learned",
|
||||
):
|
||||
super().__init__(output_dim, output_dim)
|
||||
|
||||
self.min_val = min_val
|
||||
self.max_val = max_val
|
||||
|
||||
self.embedder = NumberEmbedder(features=output_dim)
|
||||
self.embedder = NumberEmbedder(features=output_dim, fourier_features_type=fourier_features_type)
|
||||
|
||||
def forward(self, floats, device=None):
|
||||
# Cast the inputs to floats
|
||||
|
||||
533
comfy/ldm/audio/vae_sa3.py
Normal file
533
comfy/ldm/audio/vae_sa3.py
Normal file
@ -0,0 +1,533 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
import comfy.ops
|
||||
import comfy.model_management
|
||||
from comfy.ldm.modules.attention import optimized_attention
|
||||
from comfy.ldm.audio.autoencoder import WNConv1d
|
||||
|
||||
ops = comfy.ops.disable_weight_init
|
||||
|
||||
class Transpose(nn.Module):
|
||||
def forward(self, x, **kwargs):
|
||||
return x.transpose(-2, -1)
|
||||
|
||||
|
||||
def _zero_pad_modulo_sequence(x, size, dim=-2):
|
||||
input_len = x.shape[dim]
|
||||
pad_len = (size - input_len % size) % size
|
||||
if pad_len > 0:
|
||||
pad_shape = list(x.shape)
|
||||
pad_shape[dim] = pad_len
|
||||
x = torch.cat([x, torch.zeros(pad_shape, device=x.device, dtype=x.dtype)], dim=dim)
|
||||
return x
|
||||
|
||||
|
||||
def _sliding_window_mask(seq_len, window, device, dtype):
|
||||
"""Additive attention mask enforcing a ±window local window (matches flash_attn window_size)."""
|
||||
i = torch.arange(seq_len, device=device).unsqueeze(1)
|
||||
j = torch.arange(seq_len, device=device).unsqueeze(0)
|
||||
out_of_window = (j - i).abs() > window
|
||||
return torch.where(
|
||||
out_of_window,
|
||||
torch.full((1,), torch.finfo(dtype).min / 4, device=device, dtype=dtype),
|
||||
torch.zeros(1, device=device, dtype=dtype),
|
||||
)
|
||||
|
||||
|
||||
class DynamicTanh(nn.Module):
|
||||
def __init__(self, dim, init_alpha=4.0, dtype=None, device=None, **kwargs):
|
||||
super().__init__()
|
||||
self.alpha = nn.Parameter(torch.empty(1, dtype=dtype, device=device))
|
||||
self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
|
||||
self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
|
||||
|
||||
def forward(self, x):
|
||||
alpha = comfy.ops.cast_to_input(self.alpha, x)
|
||||
gamma = comfy.ops.cast_to_input(self.gamma, x)
|
||||
beta = comfy.ops.cast_to_input(self.beta, x)
|
||||
return gamma * torch.tanh(alpha * x) + beta
|
||||
|
||||
|
||||
class RotaryEmbedding(nn.Module):
|
||||
def __init__(self, dim, base=10000, base_rescale_factor=1., dtype=None, device=None):
|
||||
super().__init__()
|
||||
base = base * base_rescale_factor ** (dim / (dim - 2))
|
||||
self.register_buffer("inv_freq", torch.empty(dim // 2, dtype=dtype, device=device))
|
||||
|
||||
def forward_from_seq_len(self, seq_len, device, dtype=None):
|
||||
t = torch.arange(seq_len, device=device, dtype=torch.float32)
|
||||
return self.forward(t)
|
||||
|
||||
def forward(self, t):
|
||||
freqs = torch.outer(t.float(), comfy.model_management.cast_to(self.inv_freq, dtype=torch.float32, device=t.device))
|
||||
freqs = torch.cat((freqs, freqs), dim=-1)
|
||||
return freqs, 1.
|
||||
|
||||
|
||||
def _rotate_half(x):
|
||||
d = x.shape[-1] // 2
|
||||
return torch.cat((-x[..., d:], x[..., :d]), dim=-1)
|
||||
|
||||
|
||||
def _apply_rotary_pos_emb(t, freqs):
|
||||
out_dtype = t.dtype
|
||||
rot_dim = freqs.shape[-1]
|
||||
seq_len = t.shape[-2]
|
||||
freqs = freqs[-seq_len:]
|
||||
t_rot, t_pass = t[..., :rot_dim], t[..., rot_dim:]
|
||||
t_rot = t_rot * freqs.cos() + _rotate_half(t_rot) * freqs.sin()
|
||||
return torch.cat((t_rot.to(out_dtype), t_pass.to(out_dtype)), dim=-1)
|
||||
|
||||
|
||||
class Attention(nn.Module):
|
||||
def __init__(self, dim, dim_heads=64, qk_norm="none", qk_norm_eps=1e-6,
|
||||
differential=False, zero_init_output=True,
|
||||
dtype=None, device=None, operations=None, **kwargs):
|
||||
super().__init__()
|
||||
self.num_heads = dim // dim_heads
|
||||
self.differential = differential
|
||||
self.qk_norm = qk_norm
|
||||
|
||||
self.to_qkv = operations.Linear(
|
||||
dim, dim * (5 if differential else 3), bias=False, dtype=dtype, device=device)
|
||||
self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
if qk_norm == "dyt":
|
||||
self.q_norm = DynamicTanh(dim_heads, dtype=dtype, device=device)
|
||||
self.k_norm = DynamicTanh(dim_heads, dtype=dtype, device=device)
|
||||
elif qk_norm == "rms":
|
||||
self.q_norm = operations.RMSNorm(dim_heads, eps=qk_norm_eps, dtype=dtype, device=device)
|
||||
self.k_norm = operations.RMSNorm(dim_heads, eps=qk_norm_eps, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x, rotary_pos_emb=None, mask=None, **kwargs):
|
||||
B, N, _ = x.shape
|
||||
h = self.num_heads
|
||||
|
||||
qkv = self.to_qkv(x)
|
||||
if self.differential:
|
||||
q, k, v, q_diff, k_diff = qkv.chunk(5, dim=-1)
|
||||
del qkv
|
||||
q = q.view(B, N, h, -1).transpose(1, 2)
|
||||
k = k.view(B, N, h, -1).transpose(1, 2)
|
||||
v = v.view(B, N, h, -1).transpose(1, 2)
|
||||
q_diff = q_diff.view(B, N, h, -1).transpose(1, 2)
|
||||
k_diff = k_diff.view(B, N, h, -1).transpose(1, 2)
|
||||
else:
|
||||
q, k, v = qkv.chunk(3, dim=-1)
|
||||
del qkv
|
||||
q = q.view(B, N, h, -1).transpose(1, 2)
|
||||
k = k.view(B, N, h, -1).transpose(1, 2)
|
||||
v = v.view(B, N, h, -1).transpose(1, 2)
|
||||
|
||||
if self.qk_norm != "none":
|
||||
q_dtype, k_dtype = q.dtype, k.dtype
|
||||
q = self.q_norm(q).to(q_dtype)
|
||||
k = self.k_norm(k).to(k_dtype)
|
||||
if self.differential:
|
||||
q_diff = self.q_norm(q_diff).to(q_dtype)
|
||||
k_diff = self.k_norm(k_diff).to(k_dtype)
|
||||
|
||||
if rotary_pos_emb is not None:
|
||||
freqs, _ = rotary_pos_emb
|
||||
q_dtype, k_dtype = q.dtype, k.dtype
|
||||
q = _apply_rotary_pos_emb(q.float(), freqs).to(q_dtype)
|
||||
k = _apply_rotary_pos_emb(k.float(), freqs).to(k_dtype)
|
||||
if self.differential:
|
||||
q_diff = _apply_rotary_pos_emb(q_diff.float(), freqs).to(q_dtype)
|
||||
k_diff = _apply_rotary_pos_emb(k_diff.float(), freqs).to(k_dtype)
|
||||
|
||||
if self.differential:
|
||||
out = (optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
|
||||
- optimized_attention(q_diff, k_diff, v, h, mask=mask, skip_reshape=True))
|
||||
del q, k, v, q_diff, k_diff
|
||||
else:
|
||||
out = optimized_attention(q, k, v, h, mask=mask, skip_reshape=True)
|
||||
del q, k, v
|
||||
|
||||
return self.to_out(out)
|
||||
|
||||
|
||||
class _Sin(nn.Module):
|
||||
def forward(self, x):
|
||||
return torch.sin(3.14159265359 * x)
|
||||
|
||||
|
||||
class _GLU(nn.Module):
|
||||
def __init__(self, dim_in, dim_out, activation, dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
self.act = activation
|
||||
self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.proj(x)
|
||||
x, gate = x.chunk(2, dim=-1)
|
||||
return x * self.act(gate)
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
def __init__(self, dim, mult=4, no_bias=False, zero_init_output=True,
|
||||
sinusoidal=False, dtype=None, device=None, operations=None, **kwargs):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
act = _Sin() if sinusoidal else nn.SiLU()
|
||||
self.ff = nn.Sequential(
|
||||
_GLU(dim, inner_dim, act, dtype=dtype, device=device, operations=operations),
|
||||
nn.Identity(),
|
||||
operations.Linear(inner_dim, dim, bias=not no_bias, dtype=dtype, device=device),
|
||||
nn.Identity(),
|
||||
)
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
return self.ff(x)
|
||||
|
||||
|
||||
class TransformerBlock(nn.Module):
|
||||
def __init__(self, dim, dim_heads=64, causal=False, zero_init_branch_outputs=True,
|
||||
norm_type="dyt", add_rope=False, attn_kwargs=None, ff_kwargs=None,
|
||||
norm_kwargs=None, dtype=None, device=None, operations=None, **kwargs):
|
||||
super().__init__()
|
||||
if attn_kwargs is None:
|
||||
attn_kwargs = {}
|
||||
if ff_kwargs is None:
|
||||
ff_kwargs = {}
|
||||
if norm_kwargs is None:
|
||||
norm_kwargs = {}
|
||||
dim_heads = min(dim_heads, dim)
|
||||
|
||||
Norm = DynamicTanh if norm_type == "dyt" else operations.RMSNorm
|
||||
norm_kw = {**norm_kwargs, "dtype": dtype, "device": device}
|
||||
|
||||
self.pre_norm = Norm(dim, **norm_kw)
|
||||
self.self_attn = Attention(dim, dim_heads=dim_heads,
|
||||
zero_init_output=zero_init_branch_outputs,
|
||||
dtype=dtype, device=device, operations=operations,
|
||||
**attn_kwargs)
|
||||
self.ff_norm = Norm(dim, **norm_kw)
|
||||
self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs,
|
||||
dtype=dtype, device=device, operations=operations, **ff_kwargs)
|
||||
self.rope = RotaryEmbedding(dim_heads // 2, dtype=dtype, device=device) if add_rope else None
|
||||
|
||||
def forward(self, x, mask=None, **kwargs):
|
||||
rope = self.rope.forward_from_seq_len(x.shape[-2], device=x.device) \
|
||||
if self.rope is not None else None
|
||||
x = x + self.self_attn(self.pre_norm(x), rotary_pos_emb=rope, mask=mask)
|
||||
x = x + self.ff(self.ff_norm(x))
|
||||
return x
|
||||
|
||||
|
||||
class TransformerResamplingBlock(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, stride, type="encoder",
|
||||
transformer_depth=3, dim_heads=128, differential=True,
|
||||
sliding_window=None, chunk_size=128, chunk_midpoint_shift=False,
|
||||
dyt=True, ff_mult=3, mapping_bias=True, variable_stride=False,
|
||||
sinusoidal_blocks=0, conv_mapping=False, dtype=None, device=None, operations=None, **kwargs):
|
||||
super().__init__()
|
||||
if type not in ("encoder", "decoder"):
|
||||
raise ValueError(f"type must be 'encoder' or 'decoder', got {type!r}")
|
||||
|
||||
self.type = type
|
||||
self.stride = stride
|
||||
self.chunk_size = chunk_size
|
||||
self.chunk_midpoint_shift = chunk_midpoint_shift
|
||||
self.variable_stride = variable_stride
|
||||
self.transformer_depth = transformer_depth
|
||||
|
||||
transformer_dim = out_channels if type == "encoder" else in_channels
|
||||
|
||||
self.mapping = (WNConv1d(in_channels, out_channels, 3 if conv_mapping else 1, padding="same", bias=mapping_bias)
|
||||
if in_channels != out_channels else nn.Identity())
|
||||
|
||||
self.sliding_window_latents = sliding_window
|
||||
self.sliding_window_seq = self._get_sliding_window_size(sliding_window, stride)
|
||||
self.input_seg_size, self.output_seg_size, self.sub_chunk_size = self._get_seg_sizes(stride)
|
||||
|
||||
token_seq = 1 if variable_stride else self.output_seg_size
|
||||
self.new_tokens = nn.Parameter(torch.empty(1, token_seq, transformer_dim, dtype=dtype, device=device))
|
||||
|
||||
norm_type = "dyt" if dyt else "rms_norm"
|
||||
attn_kwargs = {"qk_norm": "dyt" if dyt else "rms", "qk_norm_eps": 1e-3,
|
||||
"differential": differential}
|
||||
norm_kwargs = {"eps": 1e-3}
|
||||
transformers = []
|
||||
for i in range(transformer_depth):
|
||||
sinusoidal = (transformer_depth - i) < sinusoidal_blocks
|
||||
transformers.append(TransformerBlock(
|
||||
transformer_dim,
|
||||
dim_heads=dim_heads,
|
||||
causal=False,
|
||||
zero_init_branch_outputs=True,
|
||||
norm_type=norm_type,
|
||||
add_rope=True,
|
||||
attn_kwargs=attn_kwargs,
|
||||
ff_kwargs={"mult": ff_mult, "no_bias": False, "sinusoidal": sinusoidal},
|
||||
norm_kwargs=norm_kwargs,
|
||||
dtype=dtype, device=device, operations=operations,
|
||||
))
|
||||
self.transformers = nn.ModuleList(transformers)
|
||||
|
||||
def _get_sliding_window_size(self, window, stride, prepend_cond_length=0):
|
||||
if window is None:
|
||||
return None
|
||||
return [w * (stride + 1 + prepend_cond_length) for w in window]
|
||||
|
||||
def _get_seg_sizes(self, stride, prepend_cond_length=0):
|
||||
sub_chunk_size = stride + 1 + prepend_cond_length
|
||||
input_seg_size = stride if self.type == "encoder" else 1
|
||||
output_seg_size = 1 if self.type == "encoder" else stride
|
||||
return input_seg_size, output_seg_size, sub_chunk_size
|
||||
|
||||
def forward(self, x, stride=None, **kwargs):
|
||||
B = x.shape[0]
|
||||
|
||||
if stride is None:
|
||||
input_seg = self.input_seg_size
|
||||
output_seg = self.output_seg_size
|
||||
sub_chunk = self.sub_chunk_size
|
||||
sliding_window = self.sliding_window_seq
|
||||
else:
|
||||
input_seg, output_seg, sub_chunk = self._get_seg_sizes(stride)
|
||||
sliding_window = self._get_sliding_window_size(self.sliding_window_latents, stride)
|
||||
|
||||
if self.type == "encoder":
|
||||
if self.transformer_depth > 0:
|
||||
pad_mod = self.chunk_size if sliding_window is None else input_seg
|
||||
x = _zero_pad_modulo_sequence(x, pad_mod, dim=-1)
|
||||
x = self.mapping(x)
|
||||
|
||||
if self.transformer_depth > 0:
|
||||
x = x.permute(0, 2, 1)
|
||||
|
||||
if self.type != "encoder":
|
||||
pad_mod = 1 if sliding_window is not None else (
|
||||
self.chunk_size // (stride if stride is not None else self.stride))
|
||||
x = _zero_pad_modulo_sequence(x, pad_mod)
|
||||
|
||||
C = x.shape[2]
|
||||
x = x.reshape(-1, input_seg, C)
|
||||
|
||||
new_tokens = self.new_tokens.expand(x.shape[0], output_seg, -1)
|
||||
x = torch.cat([x, comfy.ops.cast_to_input(new_tokens, x)], dim=-2)
|
||||
del new_tokens
|
||||
|
||||
x = x.reshape(B, -1, C)
|
||||
|
||||
if sliding_window is None:
|
||||
eff_chunk = self.chunk_size + self.chunk_size // (stride if stride is not None else self.stride)
|
||||
|
||||
if sliding_window is None and self.chunk_midpoint_shift:
|
||||
split = self.transformer_depth // 2
|
||||
shift = eff_chunk // 2
|
||||
|
||||
x = x.reshape(-1, eff_chunk, C)
|
||||
for layer in self.transformers[:split]:
|
||||
x = layer(x)
|
||||
x = x.reshape(B, -1, C)
|
||||
|
||||
shifted = torch.cat([x[:, :shift, :], x, x[:, -shift:, :]], dim=1)
|
||||
del x
|
||||
x = shifted.reshape(-1, eff_chunk, C)
|
||||
del shifted
|
||||
for layer in self.transformers[split:]:
|
||||
x = layer(x)
|
||||
x = x.reshape(B, -1, C)
|
||||
x = x[:, shift:-shift, :]
|
||||
elif sliding_window is None:
|
||||
x = x.reshape(-1, eff_chunk, C)
|
||||
for layer in self.transformers:
|
||||
x = layer(x)
|
||||
x = x.reshape(B, -1, C)
|
||||
else:
|
||||
attn_mask = _sliding_window_mask(x.shape[1], sliding_window[0], x.device, x.dtype)
|
||||
for layer in self.transformers:
|
||||
x = layer(x, mask=attn_mask)
|
||||
|
||||
x = x.reshape(-1, sub_chunk, C)
|
||||
x = x[:, -output_seg:, :]
|
||||
x = x.reshape(B, -1, C).transpose(1, 2)
|
||||
|
||||
if self.type == "decoder":
|
||||
x = self.mapping(x)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
class SAMEEncoder(nn.Module):
|
||||
def __init__(self, in_channels=2, channels=128, latent_dim=32,
|
||||
c_mults=(1, 2, 4, 8), strides=(2, 4, 8, 8),
|
||||
transformer_depths=(3, 3, 3, 3),
|
||||
dtype=None, device=None, operations=None, **kwargs):
|
||||
super().__init__()
|
||||
channel_dims = [in_channels] + [channels * c for c in c_mults]
|
||||
layers = []
|
||||
for i in range(len(c_mults)):
|
||||
layers.append(TransformerResamplingBlock(
|
||||
in_channels=channel_dims[i], out_channels=channel_dims[i + 1],
|
||||
stride=strides[i], type="encoder",
|
||||
transformer_depth=transformer_depths[i],
|
||||
dtype=dtype, device=device, operations=operations, **kwargs))
|
||||
layers += [
|
||||
Transpose(),
|
||||
operations.Linear(channel_dims[-1], latent_dim, dtype=dtype, device=device),
|
||||
Transpose(),
|
||||
]
|
||||
self.layers = nn.ModuleList(layers)
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
for layer in self.layers:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
|
||||
class SAMEDecoder(nn.Module):
|
||||
def __init__(self, out_channels=2, channels=128, latent_dim=32,
|
||||
c_mults=(1, 2, 4, 8), strides=(2, 4, 8, 8),
|
||||
transformer_depths=(3, 3, 3, 3), sinusoidal_blocks=None,
|
||||
dtype=None, device=None, operations=None, **kwargs):
|
||||
super().__init__()
|
||||
if sinusoidal_blocks is None:
|
||||
sinusoidal_blocks = [0] * len(c_mults)
|
||||
channel_dims = [out_channels] + [channels * c for c in c_mults]
|
||||
layers = [
|
||||
Transpose(),
|
||||
operations.Linear(latent_dim, channel_dims[-1], dtype=dtype, device=device),
|
||||
Transpose(),
|
||||
]
|
||||
for i in range(len(c_mults) - 1, -1, -1):
|
||||
layers.append(TransformerResamplingBlock(
|
||||
in_channels=channel_dims[i + 1], out_channels=channel_dims[i],
|
||||
stride=strides[i], type="decoder",
|
||||
transformer_depth=transformer_depths[i],
|
||||
sinusoidal_blocks=sinusoidal_blocks[i],
|
||||
dtype=dtype, device=device, operations=operations, **kwargs))
|
||||
self.layers = nn.ModuleList(layers)
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
for layer in self.layers:
|
||||
x = layer(x)
|
||||
return x
|
||||
|
||||
|
||||
class SoftNormBottleneck(nn.Module):
|
||||
def __init__(self, dim=32, noise_augment_dim=0, noise_regularize=False,
|
||||
auto_scale=False, freeze=False, dtype=None, device=None, **kwargs):
|
||||
super().__init__()
|
||||
self.noise_augment_dim = noise_augment_dim
|
||||
self.noise_regularize = noise_regularize
|
||||
self.scaling_factor = nn.Parameter(torch.empty(1, dim, 1, dtype=dtype, device=device))
|
||||
self.bias = nn.Parameter(torch.empty(1, dim, 1, dtype=dtype, device=device))
|
||||
self.noise_scaling_factor = nn.Parameter(torch.empty(1, noise_augment_dim, 1, dtype=dtype, device=device))
|
||||
if auto_scale:
|
||||
self.register_parameter("running_std", nn.Parameter(
|
||||
torch.empty(1, dtype=dtype, device=device), requires_grad=False))
|
||||
if freeze:
|
||||
for p in self.parameters():
|
||||
p.requires_grad = False
|
||||
|
||||
def encode(self, x, return_info=False, **kwargs):
|
||||
x = x * comfy.ops.cast_to_input(self.scaling_factor, x) \
|
||||
+ comfy.ops.cast_to_input(self.bias, x)
|
||||
if hasattr(self, "running_std"):
|
||||
x = x / comfy.ops.cast_to_input(self.running_std, x)
|
||||
if return_info:
|
||||
return x, {}
|
||||
return x
|
||||
|
||||
def decode(self, x, **kwargs):
|
||||
if hasattr(self, "running_std"):
|
||||
x = x * comfy.ops.cast_to_input(self.running_std, x)
|
||||
if self.noise_regularize:
|
||||
scaling = self.running_std if hasattr(self, "running_std") \
|
||||
else x.std(dim=-1, keepdim=True)
|
||||
noise = torch.randn_like(x) * comfy.ops.cast_to_input(scaling, x) * 1e-3
|
||||
x = x + noise
|
||||
if self.noise_augment_dim > 0:
|
||||
noise = comfy.ops.cast_to_input(self.noise_scaling_factor, x) * torch.randn(
|
||||
x.shape[0], self.noise_augment_dim, x.shape[-1], device=x.device, dtype=x.dtype)
|
||||
x = torch.cat([x, noise], dim=1)
|
||||
return x
|
||||
|
||||
|
||||
class PatchedPretransform(nn.Module):
|
||||
def __init__(self, channels, patch_size, **kwargs):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.patch_size = patch_size
|
||||
self.enable_grad = False
|
||||
|
||||
def _pad(self, x):
|
||||
pad_len = (self.patch_size - x.shape[-1] % self.patch_size) % self.patch_size
|
||||
if pad_len > 0:
|
||||
x = torch.cat([x, torch.zeros_like(x[:, :, :pad_len])], dim=-1)
|
||||
return x
|
||||
|
||||
def encode(self, x):
|
||||
x = self._pad(x)
|
||||
B, C, T = x.shape
|
||||
h = self.patch_size
|
||||
L = T // h
|
||||
# b c (l h) -> b (c h) l
|
||||
return x.reshape(B, C, L, h).permute(0, 1, 3, 2).reshape(B, C * h, L)
|
||||
|
||||
def decode(self, x):
|
||||
B, Ch, L = x.shape
|
||||
h = self.patch_size
|
||||
C = Ch // h
|
||||
# b (c h) l -> b c (l h)
|
||||
return x.reshape(B, C, h, L).permute(0, 1, 3, 2).reshape(B, C, L * h)
|
||||
|
||||
|
||||
class SA3AudioVAE(nn.Module):
|
||||
"""SA3 VAE. State dict keys match checkpoint after stripping 'pretransform.model.'"""
|
||||
|
||||
def __init__(self, channels=256, transformer_depths=12, sinusoidal_blocks=8,
|
||||
sliding_window=None, decoder_conv_mapping=False,
|
||||
chunk_size=128, chunk_midpoint_shift=False,
|
||||
dtype=None, device=None, operations=None):
|
||||
super().__init__()
|
||||
if operations is None:
|
||||
operations = ops
|
||||
|
||||
self.pretransform = PatchedPretransform(channels=2, patch_size=256)
|
||||
|
||||
common_kwargs = dict(
|
||||
differential=True, dyt=True, dim_heads=64,
|
||||
sliding_window=sliding_window, variable_stride=True,
|
||||
chunk_size=chunk_size, chunk_midpoint_shift=chunk_midpoint_shift,
|
||||
dtype=dtype, device=device, operations=operations,
|
||||
)
|
||||
self.encoder = SAMEEncoder(
|
||||
in_channels=512, channels=channels, c_mults=[6], strides=[16],
|
||||
latent_dim=256, transformer_depths=[transformer_depths],
|
||||
conv_mapping=False, **common_kwargs,
|
||||
)
|
||||
self.decoder = SAMEDecoder(
|
||||
out_channels=512, channels=channels, c_mults=[6], strides=[16],
|
||||
latent_dim=256, transformer_depths=[transformer_depths], sinusoidal_blocks=[sinusoidal_blocks],
|
||||
conv_mapping=decoder_conv_mapping, **common_kwargs,
|
||||
)
|
||||
self.bottleneck = SoftNormBottleneck(
|
||||
dim=256, noise_augment_dim=0, noise_regularize=True,
|
||||
auto_scale=True, freeze=True,
|
||||
dtype=dtype, device=device,
|
||||
)
|
||||
|
||||
@torch.no_grad()
|
||||
def _pretransform_encode(self, x):
|
||||
return self.pretransform.encode(x)
|
||||
|
||||
@torch.no_grad()
|
||||
def _pretransform_decode(self, x):
|
||||
return self.pretransform.decode(x)
|
||||
|
||||
def encode(self, x):
|
||||
x = self._pretransform_encode(x)
|
||||
x = self.encoder(x)
|
||||
x = self.bottleneck.encode(x)
|
||||
return x
|
||||
|
||||
def decode(self, x):
|
||||
x = self.bottleneck.decode(x)
|
||||
x = self.decoder(x)
|
||||
x = self._pretransform_decode(x)
|
||||
return x
|
||||
@ -328,7 +328,7 @@ class CrossAttention(nn.Module):
|
||||
kv = torch.cat((k, v), dim=-1)
|
||||
split_size = kv.shape[-1] // self.num_heads // 2
|
||||
|
||||
kv = kv.view(1, -1, self.num_heads, split_size * 2)
|
||||
kv = kv.view(b, -1, self.num_heads, split_size * 2)
|
||||
k, v = torch.split(kv, split_size, dim=-1)
|
||||
|
||||
q = q.view(b, s1, self.num_heads, self.head_dim)
|
||||
@ -398,7 +398,7 @@ class Attention(nn.Module):
|
||||
qkv_combined = torch.cat((query, key, value), dim=-1)
|
||||
split_size = qkv_combined.shape[-1] // self.num_heads // 3
|
||||
|
||||
qkv = qkv_combined.view(1, -1, self.num_heads, split_size * 3)
|
||||
qkv = qkv_combined.view(B, -1, self.num_heads, split_size * 3)
|
||||
query, key, value = torch.split(qkv, split_size, dim=-1)
|
||||
|
||||
query = query.reshape(B, N, self.num_heads, self.head_dim)
|
||||
@ -607,9 +607,9 @@ class HunYuanDiTPlain(nn.Module):
|
||||
def forward(self, x, t, context, transformer_options = {}, **kwargs):
|
||||
|
||||
x = x.movedim(-1, -2)
|
||||
uncond_emb, cond_emb = context.chunk(2, dim = 0)
|
||||
|
||||
context = torch.cat([cond_emb, uncond_emb], dim = 0)
|
||||
if context.shape[0] >= 2:
|
||||
uncond_emb, cond_emb = context.chunk(2, dim = 0)
|
||||
context = torch.cat([cond_emb, uncond_emb], dim = 0)
|
||||
main_condition = context
|
||||
|
||||
t = 1.0 - t
|
||||
@ -657,5 +657,8 @@ class HunYuanDiTPlain(nn.Module):
|
||||
output = self.final_layer(combined)
|
||||
output = output.movedim(-2, -1) * (-1.0)
|
||||
|
||||
cond_emb, uncond_emb = output.chunk(2, dim = 0)
|
||||
return torch.cat([uncond_emb, cond_emb])
|
||||
if output.shape[0] >= 2:
|
||||
cond_emb, uncond_emb = output.chunk(2, dim = 0)
|
||||
return torch.cat([uncond_emb, cond_emb])
|
||||
else:
|
||||
return output
|
||||
|
||||
@ -813,6 +813,85 @@ class StableAudio1(BaseModel):
|
||||
sd["{}{}".format(k, l)] = s[l]
|
||||
return sd
|
||||
|
||||
class StableAudio3(BaseModel):
|
||||
def __init__(self, model_config, seconds_total_embedder_weights, padding_embedding=None, model_type=ModelType.FLOW, device=None):
|
||||
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
|
||||
self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=384, fourier_features_type=model_config.unet_config["timestep_features_type"])
|
||||
self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
|
||||
if padding_embedding is not None:
|
||||
self.padding_embedding = torch.nn.Parameter(padding_embedding, requires_grad=False)
|
||||
else:
|
||||
self.padding_embedding = None
|
||||
|
||||
def concat_cond(self, **kwargs):
|
||||
noise = kwargs.get("noise", None)
|
||||
image = kwargs.get("concat_latent_image", None)
|
||||
|
||||
if image is None:
|
||||
shape_image = list(noise.shape)
|
||||
image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
|
||||
else:
|
||||
image = self.process_latent_in(image)
|
||||
# TODO: scale if not match
|
||||
image = utils.resize_to_batch_size(image, noise.shape[0])
|
||||
|
||||
mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
|
||||
if mask is None:
|
||||
mask = torch.zeros_like(noise)[:, :1]
|
||||
else:
|
||||
if mask.shape[1] != 1:
|
||||
mask = torch.mean(mask, dim=1, keepdim=True)
|
||||
mask = 1.0 - mask
|
||||
# TODO: scale if not match
|
||||
mask = utils.resize_to_batch_size(mask, noise.shape[0])
|
||||
|
||||
return torch.cat((mask, image), dim=1)
|
||||
|
||||
def extra_conds(self, **kwargs):
|
||||
out = {}
|
||||
|
||||
concat_cond = self.concat_cond(**kwargs)
|
||||
if concat_cond is not None:
|
||||
out['local_add_cond'] = comfy.conds.CONDNoiseShape(concat_cond)
|
||||
|
||||
noise = kwargs.get("noise", None)
|
||||
device = kwargs["device"]
|
||||
|
||||
seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 10.7666))
|
||||
seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
|
||||
|
||||
global_embed = seconds_total_embed.reshape((1, -1))
|
||||
out['global_embed'] = comfy.conds.CONDRegular(global_embed)
|
||||
|
||||
cross_attn = kwargs.get("cross_attn", None)
|
||||
if cross_attn is not None:
|
||||
cross_attn = cross_attn.to(device)
|
||||
if self.padding_embedding is not None:
|
||||
pe = self.padding_embedding.to(device=device, dtype=cross_attn.dtype)
|
||||
max_text_tokens = self.model_config.unet_config.get("max_text_tokens", 256)
|
||||
n_text = cross_attn.shape[1]
|
||||
if n_text < max_text_tokens:
|
||||
pad = pe.view(1, 1, -1).expand(cross_attn.shape[0], max_text_tokens - n_text, -1)
|
||||
cross_attn = torch.cat([cross_attn, pad], dim=1)
|
||||
cross_attn = torch.cat([cross_attn, seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
|
||||
out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
|
||||
|
||||
return out
|
||||
|
||||
def state_dict_for_saving(self, unet_state_dict, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
|
||||
sd = super().state_dict_for_saving(unet_state_dict, clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict)
|
||||
|
||||
d = {"conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()}
|
||||
|
||||
for k in d:
|
||||
s = d[k]
|
||||
for l in s:
|
||||
sd["{}{}".format(k, l)] = s[l]
|
||||
|
||||
if self.padding_embedding is not None:
|
||||
sd["conditioner.conditioners.prompt.padding_embedding"] = self.padding_embedding.data
|
||||
return sd
|
||||
|
||||
|
||||
class HunyuanDiT(BaseModel):
|
||||
def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
|
||||
|
||||
@ -116,6 +116,45 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
|
||||
if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
|
||||
unet_config = {}
|
||||
unet_config["audio_model"] = "dit1.0"
|
||||
unet_config["global_cond_dim"] = state_dict['{}to_global_embed.0.weight'.format(key_prefix)].shape[1]
|
||||
cond_embed = state_dict['{}to_cond_embed.0.weight'.format(key_prefix)]
|
||||
unet_config["project_cond_tokens"] = cond_embed.shape[0] != cond_embed.shape[1]
|
||||
unet_config["embed_dim"] = state_dict['{}to_timestep_embed.0.weight'.format(key_prefix)].shape[0]
|
||||
mem_tokens = state_dict.get('{}transformer.memory_tokens'.format(key_prefix), None)
|
||||
to_qkv = state_dict.get('{}transformer.layers.0.self_attn.to_qkv.weight'.format(key_prefix), None)
|
||||
differential = False
|
||||
if to_qkv is not None:
|
||||
if to_qkv.shape[0] == to_qkv.shape[1] * 5:
|
||||
differential = True
|
||||
if mem_tokens is not None:
|
||||
unet_config["num_memory_tokens"] = mem_tokens.shape[0]
|
||||
if '{}transformer.layers.0.self_attn.q_norm.weight'.format(key_prefix) in state_dict:
|
||||
unet_config["attn_kwargs"] = {"qk_norm": "ln", "feat_scale": True}
|
||||
rms_norm = state_dict.get('{}transformer.layers.0.self_attn.q_norm.gamma'.format(key_prefix), None)
|
||||
if rms_norm is not None:
|
||||
unet_config["attn_kwargs"] = {"qk_norm": "rms", "differential": differential}
|
||||
unet_config["norm_type"] = "rms_norm"
|
||||
unet_config["num_heads"] = unet_config["embed_dim"] // rms_norm.shape[0]
|
||||
|
||||
if '{}timestep_features.weight'.format(key_prefix) in state_dict:
|
||||
unet_config["timestep_features_type"] = "learned"
|
||||
else:
|
||||
unet_config["timestep_features_type"] = "expo"
|
||||
|
||||
io_channels = state_dict['{}postprocess_conv.weight'.format(key_prefix)].shape[0]
|
||||
unet_config["io_channels"] = io_channels
|
||||
unet_config["input_concat_dim"] = state_dict['{}transformer.project_in.weight'.format(key_prefix)].shape[1] - io_channels
|
||||
|
||||
local_add_cond = state_dict.get('{}transformer.layers.0.to_local_embed.0.weight'.format(key_prefix), None)
|
||||
if local_add_cond is not None:
|
||||
unet_config["local_add_cond_dim"] = local_add_cond.shape[1]
|
||||
|
||||
global_cond_embed = state_dict.get('{}transformer.global_cond_embedder.0.weight'.format(key_prefix), None)
|
||||
if global_cond_embed is not None:
|
||||
unet_config["global_cond_shared_embed"] = True
|
||||
unet_config["global_cond_type"] = "adaLN"
|
||||
|
||||
unet_config["depth"] = count_blocks(state_dict_keys, '{}transformer.layers.'.format(key_prefix) + '{}.')
|
||||
return unet_config
|
||||
|
||||
if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
|
||||
|
||||
@ -260,7 +260,7 @@ def resolve_cast_module_with_vbar(s, dtype, device, bias_dtype, compute_dtype, w
|
||||
|
||||
|
||||
def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None, offloadable=False, compute_dtype=None, want_requant=False):
|
||||
# NOTE: offloadable=False is a a legacy and if you are a custom node author reading this please pass
|
||||
# NOTE: offloadable=False is a legacy mode and if you are a custom node author reading this please pass
|
||||
# offloadable=True and call uncast_bias_weight() after your last usage of the weight/bias. This
|
||||
# will add async-offload support to your cast and improve performance.
|
||||
if input is not None:
|
||||
|
||||
37
comfy/sd.py
37
comfy/sd.py
@ -21,6 +21,7 @@ import comfy.ldm.ace.vae.music_dcae_pipeline
|
||||
import comfy.ldm.cogvideo.vae
|
||||
import comfy.ldm.hunyuan_video.vae
|
||||
import comfy.ldm.mmaudio.vae.autoencoder
|
||||
import comfy.ldm.audio.vae_sa3
|
||||
import comfy.pixel_space_convert
|
||||
import comfy.weight_adapter
|
||||
import yaml
|
||||
@ -67,6 +68,7 @@ import comfy.text_encoders.qwen35
|
||||
import comfy.text_encoders.ernie
|
||||
import comfy.text_encoders.gemma4
|
||||
import comfy.text_encoders.cogvideo
|
||||
import comfy.text_encoders.sa3
|
||||
|
||||
import comfy.model_patcher
|
||||
import comfy.lora
|
||||
@ -854,6 +856,34 @@ class VAE:
|
||||
self.working_dtypes = [torch.float32]
|
||||
self.disable_offload = True
|
||||
self.extra_1d_channel = 16
|
||||
elif "decoder.layers.3.transformers.0.pre_norm.alpha" in sd: # Stable Audio 3 VAE
|
||||
if "decoder.layers.3.transformers.11.self_attn.to_out.weight" in sd:
|
||||
config = {"channels": 256, "transformer_depths": 12, "sinusoidal_blocks": 8,
|
||||
"sliding_window": [1, 1], "decoder_conv_mapping": False,
|
||||
"chunk_size": 128, "chunk_midpoint_shift": False}
|
||||
self.memory_used_encode = lambda shape, dtype: (1500 * shape[2]) * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * 4096) * model_management.dtype_size(dtype)
|
||||
else:
|
||||
config = {"channels": 128, "transformer_depths": 6, "sinusoidal_blocks": 0,
|
||||
"sliding_window": None, "decoder_conv_mapping": True,
|
||||
"chunk_size": 32, "chunk_midpoint_shift": True}
|
||||
self.memory_used_encode = lambda shape, dtype: (72 * shape[2]) * model_management.dtype_size(dtype)
|
||||
self.memory_used_decode = lambda shape, dtype: (72 * shape[2] * 4096) * model_management.dtype_size(dtype)
|
||||
|
||||
self.first_stage_model = comfy.ldm.audio.vae_sa3.SA3AudioVAE(**config)
|
||||
self.latent_channels = 256
|
||||
self.output_channels = 2
|
||||
self.upscale_ratio = 4096
|
||||
self.downscale_ratio = 4096
|
||||
self.latent_dim = 1
|
||||
self.audio_sample_rate = 44100
|
||||
self.process_output = lambda audio: audio
|
||||
self.process_input = lambda audio: audio
|
||||
self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
|
||||
#This VAE has Parameters and Buffers the non-dynamic caster cannot handle
|
||||
#Force cast it for --disable-dynamic-vram users until there is a true core fix.
|
||||
if not comfy.memory_management.aimdo_enabled:
|
||||
self.disable_offload = True
|
||||
else:
|
||||
logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
|
||||
self.first_stage_model = None
|
||||
@ -1290,6 +1320,7 @@ class TEModel(Enum):
|
||||
GEMMA_4_E4B = 29
|
||||
GEMMA_4_E2B = 30
|
||||
GEMMA_4_31B = 31
|
||||
T5_GEMMA = 32
|
||||
|
||||
|
||||
def detect_te_model(sd):
|
||||
@ -1314,6 +1345,8 @@ def detect_te_model(sd):
|
||||
if weight.shape[0] == 384:
|
||||
return TEModel.BYT5_SMALL_GLYPH
|
||||
return TEModel.T5_BASE
|
||||
if "model.encoder.layers.0.pre_self_attn_layernorm.weight" in sd:
|
||||
return TEModel.T5_GEMMA
|
||||
if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
|
||||
if 'model.layers.59.self_attn.q_norm.weight' in sd:
|
||||
return TEModel.GEMMA_4_31B
|
||||
@ -1463,6 +1496,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
|
||||
else:
|
||||
clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
|
||||
clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
|
||||
elif te_model == TEModel.T5_GEMMA:
|
||||
clip_target.clip = comfy.text_encoders.sa3.SAT5GemmaModel
|
||||
clip_target.tokenizer = comfy.text_encoders.sa3.SAT5GemmaTokenizer
|
||||
tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
|
||||
elif te_model in (TEModel.GEMMA_4_E4B, TEModel.GEMMA_4_E2B, TEModel.GEMMA_4_31B):
|
||||
variant = {TEModel.GEMMA_4_E4B: comfy.text_encoders.gemma4.Gemma4_E4B,
|
||||
TEModel.GEMMA_4_E2B: comfy.text_encoders.gemma4.Gemma4_E2B,
|
||||
|
||||
@ -7,6 +7,7 @@ from . import sdxl_clip
|
||||
import comfy.text_encoders.sd2_clip
|
||||
import comfy.text_encoders.sd3_clip
|
||||
import comfy.text_encoders.sa_t5
|
||||
import comfy.text_encoders.sa3
|
||||
import comfy.text_encoders.aura_t5
|
||||
import comfy.text_encoders.pixart_t5
|
||||
import comfy.text_encoders.hydit
|
||||
@ -603,6 +604,29 @@ class StableAudio(supported_models_base.BASE):
|
||||
def clip_target(self, state_dict={}):
|
||||
return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
|
||||
|
||||
class StableAudio3(StableAudio):
|
||||
unet_config = {
|
||||
"audio_model": "dit1.0",
|
||||
"global_cond_shared_embed": True,
|
||||
}
|
||||
|
||||
sampling_settings = {
|
||||
"multiplier": 1.0,
|
||||
"shift": 2.0,
|
||||
}
|
||||
|
||||
latent_format = latent_formats.StableAudio3
|
||||
|
||||
memory_usage_factor = 7
|
||||
|
||||
def get_model(self, state_dict, prefix="", device=None):
|
||||
seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
|
||||
padding_embedding = state_dict.get("conditioner.conditioners.prompt.padding_embedding", None)
|
||||
return model_base.StableAudio3(self, seconds_total_embedder_weights=seconds_total_sd, padding_embedding=padding_embedding, device=device)
|
||||
|
||||
def clip_target(self, state_dict={}):
|
||||
return supported_models_base.ClipTarget(comfy.text_encoders.sa3.SAT5GemmaTokenizer, comfy.text_encoders.sa3.SAT5GemmaModel)
|
||||
|
||||
class AuraFlow(supported_models_base.BASE):
|
||||
unet_config = {
|
||||
"cond_seq_dim": 2048,
|
||||
@ -2018,6 +2042,7 @@ models = [
|
||||
SV3D_u,
|
||||
SV3D_p,
|
||||
SD3,
|
||||
StableAudio3,
|
||||
StableAudio,
|
||||
AuraFlow,
|
||||
PixArtAlpha,
|
||||
|
||||
207
comfy/text_encoders/sa3.py
Normal file
207
comfy/text_encoders/sa3.py
Normal file
@ -0,0 +1,207 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from comfy import sd1_clip
|
||||
from comfy.text_encoders.llama import Attention as LlamaAttention, RMSNorm, MLP, precompute_freqs_cis, apply_rope, _make_scaled_embedding
|
||||
from comfy.text_encoders.spiece_tokenizer import SPieceTokenizer
|
||||
|
||||
|
||||
class T5GemmaEncoderConfig:
|
||||
def __init__(self):
|
||||
self.vocab_size = 256000
|
||||
self.hidden_size = 768
|
||||
self.intermediate_size = 2048
|
||||
self.num_hidden_layers = 12
|
||||
self.num_attention_heads = 12
|
||||
self.num_key_value_heads = 12
|
||||
self.head_dim = 64
|
||||
self.rms_norm_eps = 1e-6
|
||||
self.rms_norm_add = False
|
||||
self.rope_theta = 10000.0
|
||||
self.attn_logit_softcapping = 50.0
|
||||
self.query_pre_attn_scalar = 64
|
||||
self.sliding_window = 4096
|
||||
self.mlp_activation = "gelu_pytorch_tanh"
|
||||
self.layer_types = ["sliding_attention", "full_attention"] * 6
|
||||
self.qkv_bias = False
|
||||
self.q_norm = None
|
||||
self.k_norm = None
|
||||
self.rms_norm_add = True
|
||||
|
||||
|
||||
class T5GemmaAttention(LlamaAttention):
|
||||
"""Reuses LlamaAttention projection setup; overrides forward for softcap attention.
|
||||
|
||||
T5Gemma applies tanh(QK^T * scale / cap) * cap between the matmul and softmax.
|
||||
This nonlinearity is incompatible with fused SDPA kernels, so attention is
|
||||
computed manually. Everything else (projections, RoPE, GQA expansion) is identical
|
||||
to LlamaAttention so __init__ is inherited unchanged.
|
||||
"""
|
||||
|
||||
def __init__(self, config, device=None, dtype=None, ops=None):
|
||||
super().__init__(config, device=device, dtype=dtype, ops=ops)
|
||||
self.scale = config.query_pre_attn_scalar ** -0.5
|
||||
self.softcap = config.attn_logit_softcapping
|
||||
|
||||
def forward(self, hidden_states, attention_mask=None, freqs_cis=None, **kwargs):
|
||||
B, S, _ = hidden_states.shape
|
||||
xq = self.q_proj(hidden_states).view(B, S, self.num_heads, self.head_dim).transpose(1, 2)
|
||||
xk = self.k_proj(hidden_states).view(B, S, self.num_kv_heads, self.head_dim).transpose(1, 2)
|
||||
xv = self.v_proj(hidden_states).view(B, S, self.num_kv_heads, self.head_dim).transpose(1, 2)
|
||||
xq, xk = apply_rope(xq, xk, freqs_cis)
|
||||
xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
|
||||
xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
|
||||
attn = torch.matmul(xq * self.scale, xk.transpose(-2, -1))
|
||||
attn = torch.tanh(attn / self.softcap) * self.softcap
|
||||
if attention_mask is not None:
|
||||
attn = attn + attention_mask
|
||||
attn = torch.nn.functional.softmax(attn.float(), dim=-1).to(xq.dtype)
|
||||
out = torch.matmul(attn, xv).transpose(1, 2).reshape(B, S, self.inner_size)
|
||||
return self.o_proj(out), None
|
||||
|
||||
|
||||
class T5GemmaBlock(nn.Module):
|
||||
def __init__(self, config, layer_type, device=None, dtype=None, ops=None):
|
||||
super().__init__()
|
||||
self.self_attn = T5GemmaAttention(config, device=device, dtype=dtype, ops=ops)
|
||||
self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
|
||||
# Names match checkpoint keys: model.encoder.layers.X.<name>.weight
|
||||
self.pre_self_attn_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=True, device=device, dtype=dtype)
|
||||
self.post_self_attn_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=True, device=device, dtype=dtype)
|
||||
self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=True, device=device, dtype=dtype)
|
||||
self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=True, device=device, dtype=dtype)
|
||||
self.is_sliding = (layer_type == "sliding_attention")
|
||||
self.sliding_window = config.sliding_window
|
||||
|
||||
def forward(self, x, attention_mask=None, freqs_cis=None):
|
||||
attn_mask = attention_mask
|
||||
if self.is_sliding and x.shape[1] > self.sliding_window:
|
||||
S = x.shape[1]
|
||||
pos = torch.arange(S, device=x.device)
|
||||
dist = (pos.unsqueeze(0) - pos.unsqueeze(1)).abs()
|
||||
sw_mask = torch.zeros(S, S, dtype=x.dtype, device=x.device)
|
||||
sw_mask.masked_fill_(dist > self.sliding_window, -torch.finfo(x.dtype).max)
|
||||
sw_mask = sw_mask.unsqueeze(0).unsqueeze(0)
|
||||
attn_mask = (attention_mask + sw_mask) if attention_mask is not None else sw_mask
|
||||
residual = x
|
||||
x = self.pre_self_attn_layernorm(x)
|
||||
x, _ = self.self_attn(x, attention_mask=attn_mask, freqs_cis=freqs_cis)
|
||||
x = self.post_self_attn_layernorm(x)
|
||||
x = residual + x
|
||||
residual = x
|
||||
x = self.pre_feedforward_layernorm(x)
|
||||
x = self.mlp(x)
|
||||
x = self.post_feedforward_layernorm(x)
|
||||
x = residual + x
|
||||
return x
|
||||
|
||||
|
||||
class T5GemmaEncoder(nn.Module):
|
||||
"""Encoder stack: embed_tokens, layers, norm.
|
||||
Keys: embed_tokens.*, layers.X.*, norm.*"""
|
||||
|
||||
def __init__(self, config, device, dtype, ops):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
# Gemma-style scaled embedding: output *= sqrt(hidden_size)
|
||||
self.embed_tokens = _make_scaled_embedding(
|
||||
ops, config.vocab_size, config.hidden_size, config.hidden_size ** 0.5, device, dtype)
|
||||
self.layers = nn.ModuleList([
|
||||
T5GemmaBlock(config, config.layer_types[i], device=device, dtype=dtype, ops=ops)
|
||||
for i in range(config.num_hidden_layers)
|
||||
])
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=True, device=device, dtype=dtype)
|
||||
|
||||
def forward(self, input_ids, attention_mask=None, embeds=None, intermediate_output=None,
|
||||
final_layer_norm_intermediate=True, dtype=None, num_layers=None):
|
||||
x = embeds if embeds is not None else self.embed_tokens(input_ids, out_dtype=dtype or torch.float32)
|
||||
seq_len = x.shape[1]
|
||||
position_ids = torch.arange(seq_len, device=x.device).unsqueeze(0)
|
||||
freqs_cis = precompute_freqs_cis(self.config.head_dim, position_ids, self.config.rope_theta, device=x.device)
|
||||
mask = None
|
||||
if attention_mask is not None:
|
||||
mask = 1.0 - attention_mask.to(x.dtype).reshape(
|
||||
(attention_mask.shape[0], 1, -1, attention_mask.shape[-1])
|
||||
).expand(attention_mask.shape[0], 1, seq_len, attention_mask.shape[-1])
|
||||
mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
|
||||
intermediate = None
|
||||
for i, layer in enumerate(self.layers):
|
||||
x = layer(x, attention_mask=mask, freqs_cis=freqs_cis)
|
||||
if i == intermediate_output:
|
||||
intermediate = x.clone()
|
||||
x = self.norm(x)
|
||||
if intermediate is not None and final_layer_norm_intermediate:
|
||||
intermediate = self.norm(intermediate)
|
||||
return x, intermediate
|
||||
|
||||
|
||||
class T5GemmaBody(nn.Module):
|
||||
"""Provides the 'encoder' sub-module.
|
||||
Keys: encoder.*"""
|
||||
|
||||
def __init__(self, config, device, dtype, ops):
|
||||
super().__init__()
|
||||
self.encoder = T5GemmaEncoder(config, device, dtype, ops)
|
||||
|
||||
|
||||
class T5GemmaModel(nn.Module):
|
||||
"""Top-level model class passed to SDClipModel as model_class.
|
||||
Module layout: self.model.encoder.* → matches checkpoint keys model.encoder.*"""
|
||||
|
||||
def __init__(self, config_dict, dtype, device, operations):
|
||||
super().__init__()
|
||||
config = T5GemmaEncoderConfig()
|
||||
self.num_layers = config.num_hidden_layers
|
||||
self.dtype = dtype
|
||||
self.model = T5GemmaBody(config, device, dtype, operations)
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.encoder.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, embeddings):
|
||||
self.model.encoder.embed_tokens = embeddings
|
||||
|
||||
def forward(self, input_ids, attention_mask=None, embeds=None, num_tokens=None,
|
||||
intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, **kwargs):
|
||||
if intermediate_output is not None and intermediate_output < 0:
|
||||
intermediate_output = self.num_layers + intermediate_output
|
||||
return self.model.encoder(
|
||||
input_ids, attention_mask=attention_mask, embeds=embeds,
|
||||
intermediate_output=intermediate_output,
|
||||
final_layer_norm_intermediate=final_layer_norm_intermediate,
|
||||
dtype=dtype, num_layers=self.num_layers)
|
||||
|
||||
|
||||
class T5GemmaSDClipModel(sd1_clip.SDClipModel):
|
||||
def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
|
||||
super().__init__(device=device, layer=layer, layer_idx=layer_idx,
|
||||
textmodel_json_config={}, dtype=dtype,
|
||||
special_tokens={"pad": 0},
|
||||
model_class=T5GemmaModel,
|
||||
enable_attention_masks=True, zero_out_masked=True,
|
||||
model_options=model_options)
|
||||
|
||||
|
||||
class T5GemmaSDTokenizer(sd1_clip.SDTokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
tokenizer_model = tokenizer_data.get("spiece_model", None)
|
||||
super().__init__(tokenizer_model, pad_with_end=False, embedding_size=768,
|
||||
embedding_key="t5gemma", tokenizer_class=SPieceTokenizer,
|
||||
has_start_token=False, has_end_token=False, pad_to_max_length=False,
|
||||
max_length=99999999, min_length=1, pad_token=0,
|
||||
tokenizer_data=tokenizer_data,
|
||||
tokenizer_args={"add_bos": False, "add_eos": False})
|
||||
|
||||
def state_dict(self):
|
||||
return {"spiece_model": self.tokenizer.serialize_model()}
|
||||
|
||||
|
||||
class SAT5GemmaTokenizer(sd1_clip.SD1Tokenizer):
|
||||
def __init__(self, embedding_directory=None, tokenizer_data={}):
|
||||
super().__init__(embedding_directory=embedding_directory,
|
||||
tokenizer_data=tokenizer_data, clip_name="t5gemma", tokenizer=T5GemmaSDTokenizer)
|
||||
|
||||
|
||||
class SAT5GemmaModel(sd1_clip.SD1ClipModel):
|
||||
def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
|
||||
super().__init__(device=device, dtype=dtype, model_options=model_options,
|
||||
name="t5gemma", clip_model=T5GemmaSDClipModel, **kwargs)
|
||||
@ -77,7 +77,7 @@ class EmptyLTXVLatentVideo(io.ComfyNode):
|
||||
@classmethod
|
||||
def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
|
||||
latent = torch.zeros([batch_size, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
|
||||
return io.NodeOutput({"samples": latent})
|
||||
return io.NodeOutput({"samples": latent, "downscale_ratio_spacial": 32})
|
||||
|
||||
generate = execute # TODO: remove
|
||||
|
||||
|
||||
@ -1,10 +1,41 @@
|
||||
import re
|
||||
import json
|
||||
import string
|
||||
from typing_extensions import override
|
||||
|
||||
from comfy_api.latest import ComfyExtension, io
|
||||
|
||||
|
||||
class StringFormat(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls) -> io.Schema:
|
||||
autogrow = io.Autogrow.TemplateNames(
|
||||
input=io.AnyType.Input("value"),
|
||||
names=list(string.ascii_lowercase),
|
||||
min=0,
|
||||
)
|
||||
return io.Schema(
|
||||
node_id="StringFormat",
|
||||
display_name="Format Text",
|
||||
category="text",
|
||||
search_aliases=["string", "format"],
|
||||
description="Same as Python's string format method. Supports all of Python's format options and features.",
|
||||
inputs=[
|
||||
io.Autogrow.Input("values", template=autogrow),
|
||||
io.String.Input("f_string", default="{a}", multiline=True),
|
||||
],
|
||||
outputs=[
|
||||
io.String.Output(),
|
||||
],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(
|
||||
cls, values: io.Autogrow.Type, f_string: str
|
||||
) -> io.NodeOutput:
|
||||
return io.NodeOutput(f_string.format(**values))
|
||||
|
||||
|
||||
class StringConcatenate(io.ComfyNode):
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
@ -413,6 +444,7 @@ class StringExtension(ComfyExtension):
|
||||
@override
|
||||
async def get_node_list(self) -> list[type[io.ComfyNode]]:
|
||||
return [
|
||||
StringFormat,
|
||||
StringConcatenate,
|
||||
StringSubstring,
|
||||
StringLength,
|
||||
|
||||
@ -1,3 +1,3 @@
|
||||
# This file is automatically generated by the build process when version is
|
||||
# updated in pyproject.toml.
|
||||
__version__ = "0.21.1"
|
||||
__version__ = "0.22.0"
|
||||
|
||||
22
openapi.yaml
22
openapi.yaml
@ -4160,6 +4160,10 @@ paths:
|
||||
name:
|
||||
type: string
|
||||
description: Display name for the API key
|
||||
description:
|
||||
type: string
|
||||
description: User-provided description of the key's purpose
|
||||
maxLength: 5000
|
||||
responses:
|
||||
"201":
|
||||
description: API key created
|
||||
@ -6351,14 +6355,6 @@ components:
|
||||
type: integer
|
||||
format: int64
|
||||
description: Size of the asset in bytes
|
||||
width:
|
||||
type: integer
|
||||
nullable: true
|
||||
description: "Original image width in pixels. Null for non-image assets or assets ingested before dimension extraction."
|
||||
height:
|
||||
type: integer
|
||||
nullable: true
|
||||
description: "Original image height in pixels. Null for non-image assets or assets ingested before dimension extraction."
|
||||
mime_type:
|
||||
type: string
|
||||
description: MIME type of the asset
|
||||
@ -7685,11 +7681,16 @@ components:
|
||||
required:
|
||||
- id
|
||||
- name
|
||||
- description
|
||||
properties:
|
||||
id:
|
||||
type: string
|
||||
name:
|
||||
type: string
|
||||
description:
|
||||
type: string
|
||||
maxLength: 5000
|
||||
description: User-provided description of the key's purpose. Always present in responses; empty string when no description was supplied on create.
|
||||
prefix:
|
||||
type: string
|
||||
description: First few characters of the key for identification
|
||||
@ -7710,12 +7711,17 @@ components:
|
||||
required:
|
||||
- id
|
||||
- name
|
||||
- description
|
||||
- key
|
||||
properties:
|
||||
id:
|
||||
type: string
|
||||
name:
|
||||
type: string
|
||||
description:
|
||||
type: string
|
||||
maxLength: 5000
|
||||
description: User-provided description of the key's purpose. Always present in responses; empty string when no description was supplied on create.
|
||||
key:
|
||||
type: string
|
||||
description: Full API key value (only returned on creation)
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
[project]
|
||||
name = "ComfyUI"
|
||||
version = "0.21.1"
|
||||
version = "0.22.0"
|
||||
readme = "README.md"
|
||||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.10"
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
comfyui-frontend-package==1.43.18
|
||||
comfyui-workflow-templates==0.9.77
|
||||
comfyui-workflow-templates==0.9.79
|
||||
comfyui-embedded-docs==0.5.0
|
||||
torch
|
||||
torchsde
|
||||
|
||||
Reference in New Issue
Block a user